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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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Applicative Model to Bring-in Conceptual Envelope for Computational thinking with Information Processes 11th

Applicative Model to Bring-in Conceptual Envelope for Comput...

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11th Annual international conference on Brain-Inspired Cognitive Architectures for Artificial Intelligence, BICA*AI 2020

作者： Wolfengagen, Viacheslav Ismailova, Larisa Kosikov, Sergey National Research Nuclear University "Moscow Engineering Physics Institute" Moscow115409 Russia NAO "JurInfoR" Moscow119435 Russia

ISBN: (纸本)9783030655952

Computational activity is now recognized as a natural science, and computational and informational processes have been discovered in the deep structures of many areas. Computations in the natural world were present long before the invention of computers, but a remarkable shift in understanding its fundamental nature occurs, in fact, before our eyes. the present moment, in fact, is a transition from the concept of computer science as an artificial science to the understanding that information processes are abundant in nature. Computing is recognized as a natural science that studies natural and artificial information processes. All object constructions are considered as virtual objects, which are possibly combinators. they are connected by "combinatory glue" and form an applicative prestructure. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cognitive interference Information process Natural computing Quantum shift learning

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Automatic Deforestation Detection based on the Deep learning in Ukraine

Automatic Deforestation Detection based on the Deep Learning...

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IEEE international Workshop on intelligent data Acquisition and Advanced Computing Systems: Technology and Applications

作者： Leonid Shumilo Mykola Lavreniuk Nataliia Kussul Bella Shevchuk National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine University of Maryland MD USA Space Research Institute NASU-SSAU Kyiv Academic University

ISBN: (纸本)9781665426060

Ukraine's big problem is the disappearance of forest cover. According to the international forest monitoring project Global Forest Watch, Ukraine lost 1.08Mha of forests from 2000 to 2020. Such sad statistics are possible only due to the lack of monitoring tools for the forest industry in Ukraine. Such a tool can be created by combining Remote Sensing and Deep learning approaches. To implement such approach for the automatic use, we combine Optical and Synthetic Aperture Radar images of the Sentinel-2 and Sentinel-1 satellite missions on which object-detection is performed using a U-Net-based neural network trained with use of the semi-supervised learning technique. this approach is being tested and shows its effectiveness in Kyiv region and going to be implemented in the same way for the Lviv, Odessa and Zakarpatya oblasts.

关键词： Deep learning Laser radar Neural networks Forestry Computer architecture Optical computing Optical imaging

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A robust and high-performance neural network for classifying landmarks in upper gastrointestinal endoscopy images 22

A robust and high-performance neural network for classifying...

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11th international Symposium on Information and Communication Technology, SoICT 2022

作者： Vu, Hai Nguyen Xuan, Cong Le, Minh Nghia Nguyen, Duc Anh Dao, Viet Hang School of Electrical and Electronics Engineering Hanoi University of Science and Technology Viet Nam Internal Medicine Faculty Hanoi Medical University Viet Nam

ISBN: (纸本)9781450397254

Gastrointestinal Endoscopy in real-Time needs to be done quickly and accurately. During an endoscopic examination, the technical doctor must spend a lot of time classifying anatomical landmarks before proceeding with further work such as lesion detection or abnormal region segmentation. the endoscopist also can ensure the completion of the examination with the help of the anatomical landmarks. In this study, we develop a robust and high-performance neural network classifying ten anatomical landmarks in the Upper Gastrointestinal tract. To this end, the proposed method consists of a Siamese Neural Network (SNN) to learn an efficient (dis)similarity metric of a pair of landmarks. We then propose to use the weight that is learned by SNN to a CNN-based structure for the feature extraction. the proposed CNN-based architecture is a lightweight model of Resnet-18 that is designed to achieve the best performance as well as a low cost of computational time. Finally, we utilize an SVM classifier to automatically classify ten anatomical landmarks from the Upper Gastrointestinal endoscopic images. the dataset used in this study is collected in four lighting modes such as (WLI) White-light Imaging, FICE (Flexible spectral imaging color enhancement), BLI (Blue Light Imaging), and LCI (Linked color imaging). the dataset is labeled by endoscopists with more than five years of experience. To enrich the dataset, we also research and evaluate augmentation techniques as effectively as possible. the augmented data includes a combination of the geometrical transforms and the data generated using CycleGAN deep learning networks. the proposed method works well with four lighting modes without depending on a certain lighting mode. It achieved 96.3% sensitivity, 99.6% specificity, and 99.3% accuracy. As a result of the computational time, we achieved approximately 60 FPS (Frame Per Second) using 20W average power with the deep learning framework Pytorch on the Jetson Xavier AGX. © 2022 ACM.

关键词： Endoscopy

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Routing Protocol Based on Q-learning in WBAN 1

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11th international conference on Communications, Signal Processing, and Systems, CSPS 2022

作者： Chen, Jiaxuan Mu, Jiasong College of Electronic and Communication Engineering Tianjin Normal University Tianjin300387 China Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission College of Electronic and Communication Engineering Tianjin Normal University Tianjin300387 China

ISBN: (数字)9789819926534

ISBN: (纸本)9789819926527

In order to solve the problems of telemedicine and human health monitoring, wireless body area network (WBAN) came into being. WBAN is a communication network based on human body, which is composed of network elements related to human body to monitor and maintain human life information. Its sensors throughout the body, make people can always get the required physiology data. Energy consumption is one of the most challenging problems in the application of WBAN due to the energy limitation of body area network devices. In order to improve the working life of nodes, this paper proposes an energy-saving routing protocol based on Q-learning. By exchanging the information between Neighbor nodes, the residual energy, communication energy consumption and routing loss are sent to the source node as feedback information, so that the source node can learn the routing strategy. the current device sets the node with the highest Q value as the destination node to find a path with less energy loss. the simulation results show that the route loss is reduced by about 15% and the average lifetime of the system is prolonged by about 10% by adding the consideration of routing loss and second-step transfer. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Routing protocols

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Sum-Product Network-Based Cardinality Estimation Research 5th

Sum-Product Network-Based Cardinality Estimation Research

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5th international conference on Artificial Intelligence in China, AIC 2023

作者： Tie, Jun Yang, Hanchao Cheng, Yong Sun, Chong Pan, Ziyi College of Computer Science South-Central Minzu University Wuhan430074 China Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Enterprises Wuhan430074 China Hubei Provincial Engineering Research Centre for Agricultural Blockchain and Intelligent Management Wuhan430074 China Beijing Qingtai Data Technology Co. Ltd. Beijing100000 China

ISBN: (纸本)9789819975440

As data distribution is non-uniform and attribute correlations vary in strength, challenges are encountered when using learning-based cardinality estimation methods in database query optimization. An improved algorithm based on the sum-product network was proposed to address these issues. the algorithm has two improvements: a self-adaptive clustering selection algorithm (SCS) for the clustering algorithm module and an RDC-based automated Search for a Hyperparameter Tuning method (RDC-ASHT) for the attribute partitioning module. SCS selects the best clustering method, improves the ability to partition and merge clusters, and validates the performance of the grid-based clustering method. RDC-ASHT searches for optimal threshold intervals, reducing computational resources and time costs and enabling faster discovery of the optimal solution. Experimental results demonstrate that the improved model has better speed and generalization performance than the original and can accurately estimate cardinalities. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Clustering algorithms

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Brain Tumor Detection Using Quantum Neural Network 11th

Brain Tumor Detection Using Quantum Neural Network

引用

11th international conference on intelligent Computing and Applications, ICRTC 2023

作者： Arjaria, Siddhartha Kumar Gupta, Ashwani Mishra, Paritosh Singh, Harsh Gupta, Shivam Gupta, Nikita Rajkiya Engineering College Banda Uttar Pradesh Banda India

ISBN: (纸本)9789819717231

According to American Society of Clinical Oncology, in 2020, around 300 thousand people were diagnosed with brain or spinal cord tumor(s) worldwide. For effective treatment of such ailments, timely detection is of utmost importance. Currently, doctors detect tumors manually by analyzing the MRI images of a patient. For the most effective treatment, it is expected that doctors crosscheck and re-verify the accuracy of their diagnosis. In the era of machine learning, mathematical models are being used to simplify decision making. In the presented work, a hybrid quantum convolution neural network is being proposed for the detection of brain tumors. the presented work uses publicly available Kaggle dataset(s) of Magnetic Resonance Imaging (MRI) images for the binary classification of brain tumors using hybrid quantum convolution neural networks (HQCNN). there are two types of layers used in the presented model. the quantum layer is used for faster computation and the classical layer takes the output of the quantum layer as input and processes it to produce classification output. the proposed model has achieved an accuracy of about 94%. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Magnetic resonance imaging

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Finite Element Analysis of a Magnetocell for MR Elastomers

Finite Element Analysis of a Magnetocell for MR Elastomers

引用

IEEE international Workshop on intelligent data Acquisition and Advanced Computing Systems: Technology and Applications

作者： Zuzanna Mielnik Denys Gutenko Faculty Electrical and Computer Engineering Cracow University of Technology Krakow Poland

ISBN: (纸本)9781665426060

Magnetorheological (MR) elastomers (MREs) are representatives of smart materials well known for its field-dependent stiffness. therefore, it is important to develop and analyze new methods for characterizing its mechanical properties. this paper will look into the design aspects of a magnetocell for measuring the field-dependence of the stiffness of an MR elastomer. the authors develop a finite-element model of the magnetocell, then reveal the obtained results as flux density maps and graphs of various quantities.

关键词： Magnetic flux Magnetic field measurement Magnetostatics conferences data acquisition Mechanical factors Magnetic analysis

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Secondary user QoE enhancement through learning based predictive spectrum access in cognitive radio networks 1

引用

11th international conference on Cognitive Radio Oriented Wireless Networks, CROWNCOM 2016

作者： Agarwal, Anirudh Dubey, Shivangi Gangopadhyay, Ranjan Debnath, Soumitra Department of ECE The LNM Institute of Information Technology Jaipur India

ISBN: (数字)9783319403526

ISBN: (纸本)9783319403519

Quality of experience (QoE) of a secondary spectrum user is mainly governed by its spectrum utilization, the energy consumption in spectrum sensing and the impact of channel switching in a cognitive radio network. It can be enhanced by prediction of spectrum availability of different channels in the form of their idle times through historical information of primary users’ activity. Based on a reliable prediction scheme, the secondary user chooses the channel with the longest idle time for transmission of its data. In contrast to the existing method of statistical prediction, the use and applicability of supervised learning based prediction in various traffic scenarios have been studied in this paper. Prediction accuracy is investigated for three machine learning techniques, artificial neural network based Multilayer Perceptron (MLP), Support Vector Machines (SVM) with Linear Kernel and SVM with Gaussian Kernel, among which, the best one is chosen for prediction based opportunistic spectrum access. the results highlight the analysis of the learning techniques with respect to the traffic intensity. Moreover, a significant improvement in spectrum utilization of the secondary user with reduction in sensing energy and channel switching has been found in case of predictive dynamic channel allocation as compared to random channel selection. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2016.

关键词： Forecasting

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A Satellite-Based Rainfall Prediction Model Using Convolution Neural Networks 11th

A Satellite-Based Rainfall Prediction Model Using Convolutio...

引用

11th international conference on Frontiers of intelligent Computing: theory and Applications, FICTA 2023

作者： Lakshmi Sujitha, T. Anuradha, T. Akshitha, G. Department of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada India

ISBN: (纸本)9789819967018

Agriculture, transportation, forestry, and tourism are just a few of the human efforts that are impacted by rainfall, which is a climatic factor. As Rainfall is the climatic element that governments have all voiced worry about how challenging it is to predict when it will rain. Because it is most frequently associated with undesirable natural events including landslides, flooding, mass movements, and avalanches, rainfall prediction is essential in this respect. these events have had a lasting effect on society. therefore, by implementing a reliable method for predicting rainfall, it is feasible to take precautionary measures for these natural occurrences. Because meteorological systems are not consistently recognized across time, predicting rainfall is difficult. this study forecasts the rainfall using deep learning techniques. As an outcome, in this research, we propose a model for forecasting the rate of rainfall using the convolution neural network (CNN) technique. As a consequence, we were able to generate rainfall model with an average root mean squared error of 2.82% and a standardized mean absolute error of 2.43%. Our method is unique in that we have developed a neural network-based model that uses cyclone datasets to forecast the rate of precipitation. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Rain

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A memetic kernel clustering algorithm for change detection in SAR images

A memetic kernel clustering algorithm for change detection i...

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11th international conference on Bio-inspired Computing – theories and Applications, BIC-TA 2016

作者： Li, Yangyang Lu, Gao Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Joint International Research Laboratory of Intelligent Perception and Computation Xidian University XianShaanxi710071 China

ISBN: (纸本)9789811036132

For SAR images change detection problem, a memetic kernel clustering algorithm (MKCA) is proposed. SAR image change detection problem as an optimization problem can solved by kernel clustering method. In this paper, the kernel function can transform nonlinear data into a high dimensional feature space, and increases the probability of the linear separability of the patterns within the transformed space and simplifies the associated data structure. Meanwhile local learning operators are designed to further enhance the ability of global exploration and promote performance of classification. Finally, to evaluate this method, the performance of the proposed method has been compared with some classical clustering algorithms for change detection in SAR (synthetic aperture radar) images. the experiments showed that the proposed algorithm can reduce the number of missed alarms and false alarms. So the proposed algorithm can effectively separate the unchanged area and the changed area. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Clustering algorithms

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